Abstract Deep Transfer Learning (DTL) is a new paradigm of machine learning, which can
not only leverage the advantages of Deep Learning (DL) in feature representation, but also …

Edge computing is an emerging paradigm that offloads the computations and analytics
workloads onto the Internet of Things (IoT) edge devices to accelerate the computation …

As a multi-resolution analysis method rooted rigorously in mathematics, wavelet transform
(WT) has shown its great potential in rotary machine fault diagnosis, characterized by …

We develop a novel deep learning framework to achieve highly accurate machine fault
diagnosis using transfer learning to enable and accelerate the training of deep neural …

This paper reviews the recent literature on machine learning (ML) models that have been
used for condition monitoring in wind turbines (eg blade fault detection or generator …

Fault diagnosis of rotating machinery plays a significant role for the reliability and safety of
modern industrial systems. As an emerging field in industrial applications and an effective …

Wind turbines are widely installed as the new source of cleaner energy production. Dynamic
and random stress imposed on the generator bearing of a wind turbine may lead to …

This paper proposes a novel intelligent fault diagnosis method to automatically identify
different health conditions of wind turbine (WT) gearbox. Unlike traditional approaches …

With significant advancement in information technologies, Digital Twin has gained
increasing attention as it offers an enabling tool to realise digitally-driven, cloud-enabled …

Timely and accurate bearing fault detection and diagnosis is important for reliable and safe
operation of industrial systems. In this study, performance of a generic real-time induction …